IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v138y2019icp292-308.html
   My bibliography  Save this article

The AXIOM approach for probabilistic and causal modeling with expert elicited inputs

Author

Listed:
  • Panula-Ontto, Juha

Abstract

Expert informants can be used as the principal information source in the modeling of socio-techno-economic systems or problems to support planning, foresight and decision-making. Such modeling is theory-driven, grounded in expert judgment and understanding, and can be contrasted with data-driven modeling approaches. Several families of approaches exist to enable expert elicited systems modeling with varying input information requirements and analytical ambitions.

Suggested Citation

  • Panula-Ontto, Juha, 2019. "The AXIOM approach for probabilistic and causal modeling with expert elicited inputs," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 292-308.
  • Handle: RePEc:eee:tefoso:v:138:y:2019:i:c:p:292-308
    DOI: 10.1016/j.techfore.2018.10.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162518305870
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techfore.2018.10.006?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Margherita Pagani, 2009. "Roadmapping 3G mobile TV : Strategic thinking and scenario planning through repeated cross-impact handling," Post-Print hal-02313094, HAL.
    2. T Ritchey, 2006. "Problem structuring using computer-aided morphological analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(7), pages 792-801, July.
    3. Bañuls, Victor A. & Turoff, Murray & Hiltz, Starr Roxanne, 2013. "Collaborative scenario modeling in emergency management through cross-impact," Technological Forecasting and Social Change, Elsevier, vol. 80(9), pages 1756-1774.
    4. Panula-Ontto, J. & Piirainen, K.A., 2018. "EXIT: An alternative approach for structural cross-impact modeling and analysis," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 89-100.
    5. Ford, David N. & Sterman, John., 1997. "Expert knowledge elicitation to improve mental and formal models," Working papers WP 3953-97., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    6. Panula-Ontto, Juha & Luukkanen, Jyrki & Kaivo-oja, Jari & O'Mahony, Tadhg & Vehmas, Jarmo & Valkealahti, Seppo & Björkqvist, Tomas & Korpela, Timo & Järventausta, Pertti & Majanne, Yrjö & Kojo, Matti , 2018. "Cross-impact analysis of Finnish electricity system with increased renewables: Long-run energy policy challenges in balancing supply and consumption," Energy Policy, Elsevier, vol. 118(C), pages 504-513.
    7. Johansen, Iver, 2018. "Scenario modelling with morphological analysis," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 116-125.
    8. Manuele Leonelli & James Smith, 2015. "Bayesian decision support for complex systems with many distributed experts," Annals of Operations Research, Springer, vol. 235(1), pages 517-542, December.
    9. D. Thorleuchter & D. Van Den Poel & A. Prinzie & -, 2010. "A compared R&D-based and patent-based cross impact analysis for identifying relationships between technologies," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/632, Ghent University, Faculty of Economics and Business Administration.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Panula-Ontto, Juha & Luukkanen, Jyrki & Kaivo-oja, Jari & O'Mahony, Tadhg & Vehmas, Jarmo & Valkealahti, Seppo & Björkqvist, Tomas & Korpela, Timo & Järventausta, Pertti & Majanne, Yrjö & Kojo, Matti , 2018. "Cross-impact analysis of Finnish electricity system with increased renewables: Long-run energy policy challenges in balancing supply and consumption," Energy Policy, Elsevier, vol. 118(C), pages 504-513.
    2. Roland Broll & Gerald Blumberg & Christoph Weber, "undated". "Thesenpapier: Constructing Consistent Energy Scenarios using Cross Impact Matrices," EWL Working Papers 2005, University of Duisburg-Essen, Chair for Management Science and Energy Economics.
    3. Panula-Ontto, J. & Piirainen, K.A., 2018. "EXIT: An alternative approach for structural cross-impact modeling and analysis," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 89-100.
    4. Cheng, M.N. & Wong, Jane W.K. & Cheung, C.F. & Leung, K.H., 2016. "A scenario-based roadmapping method for strategic planning and forecasting: A case study in a testing, inspection and certification company," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 44-62.
    5. D. Thorleuchter & D. Van Den Poel, 2013. "Semantic Compared Cross Impact Analysis," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/862, Ghent University, Faculty of Economics and Business Administration.
    6. Seeve, Teemu & Vilkkumaa, Eeva, 2022. "Identifying and visualizing a diverse set of plausible scenarios for strategic planning," European Journal of Operational Research, Elsevier, vol. 298(2), pages 596-610.
    7. Klerkx, Rik & Pelsser, Antoon, 2022. "Narrative-based robust stochastic optimization," Journal of Economic Behavior & Organization, Elsevier, vol. 196(C), pages 266-277.
    8. D. Thorleuchter & D. Van Den Poel, 2013. "Quantitative Cross Impact Analysis with Latent Semantic Indexing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/861, Ghent University, Faculty of Economics and Business Administration.
    9. Lee, Changyong & Kim, Juram & Lee, Sungjoo, 2016. "Towards robust technology roadmapping: How to diagnose the vulnerability of organisational plans," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 164-175.
    10. Margherita, Alessandro & Elia, Gianluca & Klein, Mark, 2021. "Managing the COVID-19 emergency: A coordination framework to enhance response practices and actions," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    11. Altuntas, Serkan & Dereli, Turkay & Kusiak, Andrew, 2015. "Analysis of patent documents with weighted association rules," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 249-262.
    12. Jodlbauer, Herbert & Tripathi, Shailesh & Brunner, Manuel & Bachmann, Nadine, 2022. "Stability of cross impact matrices," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    13. de Alcantara, Douglas Pedro & Martens, Mauro Luiz, 2019. "Technology Roadmapping (TRM): a systematic review of the literature focusing on models," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 127-138.
    14. Soon Goo Hong & DonHee Lee, 2023. "Development of a citizen participation public service innovation model based on smart governance," Service Business, Springer;Pan-Pacific Business Association, vol. 17(3), pages 669-694, September.
    15. Kemp-Benedict, Eric & Carlsen, Henrik & Kartha, Sivan, 2019. "Large-scale scenarios as ‘boundary conditions’: A cross-impact balance simulated annealing (CIBSA) approach," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 55-63.
    16. Xiaojiao Qiao & Dan Shi, 2019. "Risk Analysis of Emergency Based on Fuzzy Evidential Reasoning," Complexity, Hindawi, vol. 2019, pages 1-10, November.
    17. Hansen, Christoph & Daim, Tugrul & Ernst, Horst & Herstatt, Cornelius, 2016. "The future of rail automation: A scenario-based technology roadmap for the rail automation market," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 196-212.
    18. Cho, Yonghee & Yoon, Seong-Pil & Kim, Karp-Soo, 2016. "An industrial technology roadmap for supporting public R&D planning," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 1-12.
    19. Jang, Hyun Jin & Woo, Han-Gyun & Lee, Changyong, 2017. "Hawkes process-based technology impact analysis," Journal of Informetrics, Elsevier, vol. 11(2), pages 511-529.
    20. Jannie Coenen & Rob van der Heijden & Allard C. R. van Riel, 2019. "Making a Transition toward more Mature Closed-Loop Supply Chain Management under Deep Uncertainty and Dynamic Complexity: A Methodology," Sustainability, MDPI, vol. 11(8), pages 1-27, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:138:y:2019:i:c:p:292-308. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.